USING ARTIFICIAL INTELLIGENCE TECHNOLOGIES IN THE MANAGEMENT OF MULTIPLE SCLEROSIS
Multiple Sclerosis is a chronic autoimmune disease characterized by heterogeneous clinical manifestations.. In recent years, the application of artificial ıntelligence methods, particularly machine learning and deep learning techniques, has opened new opportunities for multiple sclerosis manage...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Information Technology Publishing House
2025-07-01
|
| Series: | Problems of Information Society |
| Online Access: | https://jpis.az/uploads/article/en/2025_2/USING_ARTIFICIAL_%C4%B0NTELLIGENCE_TECHNOLOGIES_IN_THE_MANAGEMENT_OF_MULTIPLE_SCLEROSIS.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Multiple Sclerosis is a chronic autoimmune disease characterized by heterogeneous
clinical manifestations.. In recent years, the application of artificial ıntelligence
methods, particularly machine learning and deep learning techniques, has opened
new opportunities for multiple sclerosis management. In this study, the potential of
artificial ıntelligence for multiple sclerosis diagnostics, treatment and prognosis is
estimated.The research found that RNN models excel in long-term disease
progression modeling by effectively capturing temporal sequences. Random Forest
and XGBoost models accurately predict relapse risks and the probability of disease
progression. The MindGlide platform accelerates MRI analysis, while CDSS
facilitates the optimization of personalized treatment decisions. Biomarker-based
models offer new avenues for early detection of the disease at the subclinical stage.
Overall, hybrid model approaches integrating clinical, radiological, and molecular
data present a promising pathway for personalized multiple sclerosis management
and the development of early intervention strategies. |
|---|---|
| ISSN: | 2077-964X 2309-7566 |